U.S. Court Rejects Structural Remedies for Google AdX: Perhaps Not a Surprise

The U.S. District Court in Virginia has rejected the U.S. Department of Justice structural remedies in the case of Google advertising antitrust, and instead ordered behavioral remedies. 

The DoJ had asked for divestiture of AdX, among other remedies. Some would have questioned whether divestiture and untangling was feasible, in any case.  

A forced divestiture would likely have meant:

• Possible loss of about 4.1 percent of Google's revenue and 1.5 percent of operating profit (2020 estimate)

• Loss of vertical integration (ability to run the ad server Google Ad Manager, the exchange (AdX) and the buy-side tools all in one stack)

• Losing AdX privileged access to ad server auction data and demand

• Losing capabilities such as  "last look" that advantaged AdX bids over rival exchanges

• Losing the ability to steer publisher and advertiser demand toward its own exchange by default

• Losing a business moat compared to Xandr (Microsoft), PubMatic, Magnite or OpenX.

The financial hit from losing AdX's direct revenue arguably would have been modest. The larger implications were competitive: 

• Losing the ability to internally route demand and auction advantages toward its own exchange

• Losing market share to rivals in the near term

• Facing execution risk from a messy technical separation.

Divestiture would not have affected Google's dominant position in the broader digital ad market (search, YouTube, Google Ads), as none of those were alleged to be monopolies. 

The actual behavioral remedies will be agreed upon by Alphabet and DoJ over the next month. 

Court watchers might have bet on behavioral rather than structural remedies. 

In modern U.S. computing history, courts and agencies overwhelmingly settle on behavioral remedies even after finding liability, and the handful of times a true structural breakup was ordered, it either got overturned on appeal or never survived to implementation. 

The one clean exception is AT&T in 1982 (not a "computing" company, but the antecedent case for how computing cases are usually discussed).

Case	Period	Allegation	Remedy Sought	Outcome	Type
United States v. AT&T (1956 consent decree)	1949–1956	Monopolizing telecom equipment	DOJ sought breakup	Settled: AT&T confined to regulated telephone business, barred from computing/commercial ventures	Behavioral
United States v. IBM	1969–1982	Monopolizing mainframe computing	DOJ sought full breakup	DOJ voluntarily dismissed the case in 1982 as "without merit"	None (dropped)
United States v. AT&T	1974–1982	Monopolizing local/long-distance telephony	DOJ sought breakup	Settled via consent decree: AT&T split into seven regional "Baby Bells"	Structural
United States v. Microsoft	1998–2001	Monopoly maintenance (browser tying)	DOJ sought company split (OS vs. applications)	District court ordered breakup (2000); reversed on appeal; settled 2001 on conduct terms	Behavioral (final)
European Commission v. Microsoft	2004	Abuse of dominance (Windows Media Player tying, interoperability)	Conduct remedies + unbundling	Fine + required unbundled Windows version and interoperability disclosures	Behavioral (with a quasi-structural unbundling element)
FTC v. Intel	2009–2010	Exclusionary dealing with OEMs	Behavioral remedies	Settled via consent order; no divestiture	Behavioral
FTC v. Qualcomm	2017–2020	Exclusionary licensing practices	Injunctive/behavioral remedies	9th Circuit reversed district court; FTC lost entirely	None (FTC lost)
EU v. Google (Shopping, Android, AdSense)	2017–2019	Self-preferencing, Android bundling, ad exclusivity	Conduct remedies + fines	Fines (~€8B combined) plus behavioral conduct changes; no breakup	Behavioral
United States v. Google (Search)	2020–2025	Illegal monopoly via default-placement deals	DOJ sought Chrome/Android divestiture	Judge Mehta (Sept. 2025) denied divestiture; ordered data-sharing and end to exclusive default contracts	Behavioral
United States v. Google (Ad Tech)	2023–2026	Illegal tying of ad server and exchange	DOJ sought AdX divestiture	Judge Brinkema (Sept. 2026) denied divestiture; ordered behavioral remedies	Behavioral
FTC v. Meta	2020–2025	Illegal monopoly via "buy or bury" acquisitions	FTC sought Instagram/WhatsApp divestiture	Judge Boasberg (Nov. 2025) ruled FTC failed to prove current monopoly power; case dismissed	None (FTC lost)

Of eleven major computing/telecom cases spanning roughly 70 years, only the 1982 AT&T case resulted in an actual, implemented structural remedy. 

Microsoft's breakup was ordered but reversed before it took effect. 

Three of the most recent, highest-profile cases (Google Search, Google Ad Tech, Meta) all had the DOJ or Federal Trade Commission explicitly request divestiture, and in every one of them the court either declined to order it or ruled the government hadn't proven its case at all.

Courts in Microsoft, Google Search, and Google Ad Tech all cited the risk of "incredibly messy and highly risky" separations of deeply integrated software/data systems. Judge Amit  Mehta used almost that exact language on Chrome, and Judge Lconic Brinkema's opinion in the AdX case echoed Google's own arguments about technical infeasibility.

Judge Mehta explicitly distinguished growth from "superior product, business acumen, or historic accident" versus growth from illegal conduct, and found Google's dominance wasn't attributable enough to the violation to justify divestiture.

In the Google ad tech case, testimony raised real doubt about whether a workable buyer even existed for AdX, since a divested asset built to be part of one company's stack often isn't viable standing alone.

Fast-moving markets are another issue. Judge James Boasberg's Meta ruling leaned on the idea that computing markets change too quickly for old monopoly findings to still describe today's competitive reality, undermining the case for any remedy, structural or not.

AI Value Migration Should Resemble Prior Computing Trends

To the extent that the value of generative artificial intelligence models is based on computational power or speed, it is virtually inevitable that raw processing power will cease to be the driver of customer value as the differences in performance between models diminishes and as open source alternatives proliferate. 

We have seen that shift in many types of computing products. Inference costs, for example, dropped 600 times between 2020 and 2026, for example. The price of the cheapest available output tokens fell from roughly $0.13 per million tokens in mid-2024 into the $0.01-$0.03 range in 2025-2026, according to one study. 

In the personal computer industry, that meant marketing eventually shifted away from processor speed to other attributes, while the overall value shifted to applications. 

So we might well predict that the cost of using models will continue to drop, while model value also shifts. The likely outcome is that, as cheaper computation expanded the addressable markets for computation, so cheaper inference will grow the addressable use cases for inference. 

 

PC era	AI era
CPU cycles	Tokens
MHz/GHz	Model intelligence
RAM/storage	Context/knowledge
Faster processor	Better model
Cheaper computing	Cheaper inference
PC hardware commoditization	Model/token commoditization
Software captures value	Applications/agents capture value

If typical computing product models also apply, then value will migrate “up the stack.” Instead of evaluating inputs (processor speed; model power), we shift to evaluating outputs “what does it do for me?” or “what are the economic results?”). 

Eventually, we stop evaluating value in discrete ways, as capabilities are simply integrated into many other products. The analogy perhaps is electricity, an input used by many products, but not itself a user-relevant output. 

The implications for value in the AI value chain would seem to be clear as well. Over time, value gets produced beyond workflows or even outcomes. At some point, AI becomes invisible, as electricity supply is invisible. 

We assume its existence, as we assume networking exists, or computation exists. 

At that point, AI becomes infrastructure for other products, the way electricity, computation and networking are available for use by many types of products. 

It might take some time, but the PC analogy also suggests the evolution path for AI. When computation was scarce, computation itself was valuable.

When computation became abundant, software became valuable. When software became abundant, data, networks, platforms and workflows became increasingly valuable.

When intelligence becomes abundant, the scarce resource may become the ability to direct intelligence toward economically valuable outcomes, as arguably was true not only of PCs but also transistors and optical fiber networks. 

Scarcity is the driver. Early on, inference capability is scarce, so that drives the value metrics. Later, when inference is plentiful, scarcity shifts elsewhere: “what are the outcomes?”

On the other hand, the value of some frontier models should remain, as commodity PCs coexist and embedded processors coexist with graphics processing units and servers. One popular example might be smartphones. 

Smartphones illustrate that the physical device can remain the value-bearing product even after its underlying computing capabilities become commoditized. The reason is that the smartphone bundles computing with several other scarce things.

PCs remain “place based.” They sit on desks. We use them for work, learning or play. Smartphones are used ambiently and personally, with sensors, cameras and location awareness that make them a platform “for life.” 

Component	Historically scarce	Today
CPU	Computing power	Increasingly commoditized
Storage	Capacity	Increasingly cheap
Display	Resolution/size	Mature technology
Camera	Image quality	Still highly differentiated
Battery	Energy density	Still constrained
Radio	Connectivity	Increasingly standardized
Sensors	Capabilities	Cheap but useful
Software	Basic functionality	Ecosystem differentiator
Industrial design	Physical experience	Still differentiated
Network	Connectivity	Major source of utility
Ecosystem	Applications/services	Extremely valuable
Convenience	Always-available computing	Very valuable

The point is that value migrates toward whatever remains scarce. For PCs, scarcity migrated toward software and applications.

For smartphones, it migrated toward ecosystems, connectivity, design, cameras, convenience and network effects.

For AI, the scarce things might be context, proprietary data, customer relationships, trust, workflow integration, distribution and the ability to turn intelligence into economically valuable action.

Actually, Data Center Projects are Mostly on Track

Though opposition to new data centers now is effectively a moral panic, it does not appear that public opposition or public official actions are going to slow deployment, as logical as that might seem. 

To be sure, data center project cancellations seem to get the headlines. According to Heatmap, more than 100 data center projects have been canceled this year in the face of local opposition, while more than 200 are currently being fought.

In the first quarter of this year, at least 3.5 gigawatts of data center capacity were canceled amid local opposition, according to Heatmap. During the same three months, at least 36 GW of capacity were added to the US pipeline of proposed and active projects, according to analytics firm Wood Mackenzie.

source: Vox 

In other words, there is no evidence of major growth delays, as shown by Semianalysis data. Despite some popular claims that half of data center projects have been cancelled, that is untrue.  

source: Semianalysis